Browsing by Author "Higdon, Dave"
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- Forecasting influenza activity using machine-learned mobility mapVenkatramanan, Srinivasan; Sadilek, Adam; Fadikar, Arindam; Barrett, Christopher L.; Biggerstaff, Matthew; Chen, Jiangzhuo; Dotiwalla, Xerxes; Eastham, Paul; Gipson, Bryant; Higdon, Dave; Kucuktunc, Onur; Lieber, Allison; Lewis, Bryan L.; Reynolds, Zane; Vullikanti, Anil Kumar S.; Wang, Lijing; Marathe, Madhav V. (2021-02-09)Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model's performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model's ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology. Human mobility plays a central role in the spread of infectious diseases and can help in forecasting incidence. Here the authors show a comparison of multiple mobility benchmarks in forecasting influenza, and demonstrate the value of a machine-learned mobility map with global coverage at multiple spatial scales.
- Using data-driven agent-based models for forecasting emerging infectious diseasesVenkatramanan, Srinivasan; Lewis, Bryan L.; Chen, Jiangzhuo; Higdon, Dave; Vullikanti, Anil Kumar S.; Marathe, Madhav V. (Elsevier, 2017-02-22)Producing timely, well-informed and reliable forecasts for an ongoing epidemic of an emerging infectious disease is a huge challenge. Epidemiologists and policy makers have to deal with poor data quality, limited understanding of the disease dynamics, rapidly changing social environment and the uncertainty on effects of various interventions in place. Under this setting, detailed computational models providea comprehensive framework for integrating diverse data sources into a well-defined model of disease dynamics and social behavior, potentially leading to better understanding and actions. In this paper,we describe one such agent-based model framework developed for forecasting the 2014–2015 Ebola epidemic in Liberia, and subsequently used during the Ebola forecasting challenge. We describe the various components of the model, the calibration process and summarize the forecast performance across scenarios of the challenge. We conclude by highlighting how such a data-driven approach can be refinedand adapted for future epidemics, and share the lessons learned over the course of the challenge.